Please use this identifier to cite or link to this item: https://doi.org/10.2174/138955706776361501
Title: Prediction of compounds with specific pharmacodynamic, pharmacokinetic or toxicological property by statistical learning methods
Authors: Yap, C.W. 
Xue, Y.
Li, H. 
Li, Z.R. 
Ung, C.Y. 
Han, L.Y. 
Zheng, C.J. 
Cao, Z.W.
Chen, Y.Z. 
Keywords: Molecular descriptors
Pharmacodynamic
Pharmacokinetic
QSAR
QSPR
Statistical learning methods
Structural diversity
Toxicology
Issue Date: Apr-2006
Citation: Yap, C.W., Xue, Y., Li, H., Li, Z.R., Ung, C.Y., Han, L.Y., Zheng, C.J., Cao, Z.W., Chen, Y.Z. (2006-04). Prediction of compounds with specific pharmacodynamic, pharmacokinetic or toxicological property by statistical learning methods. Mini-Reviews in Medicinal Chemistry 6 (4) : 449-459. ScholarBank@NUS Repository. https://doi.org/10.2174/138955706776361501
Abstract: Computational methods for predicting compounds of specific pharmacodynamic, pharmacokinetic, or toxicological property are useful for facilitating drug discovery and drug safety evaluation. The quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) methods are the most successfully used statistical learning methods for predicting compounds of specific property. More recently, other statistical learning methods such as neural networks and support vector machines have been explored for predicting compounds of higher structural diversity than those covered by QSAR and QSPR. These methods have shown promising potential in a number of studies. This article is intended to review the strategies, current progresses and underlying difficulties in using statistical learning methods for predicting compounds of specific property. It also evaluates algorithms commonly used for representing structural and physicochemical properties of compounds. © 2006 Bentham Science Publishers Ltd.
Source Title: Mini-Reviews in Medicinal Chemistry
URI: http://scholarbank.nus.edu.sg/handle/10635/77632
ISSN: 13895575
DOI: 10.2174/138955706776361501
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